{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "084043df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tkinter import *\n",
    "from tkinter import filedialog\n",
    "from PIL import Image, ImageTk\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout\n",
    "import tensorflow as tf\n",
    "\n",
    "# 设置 CPU 多线程\n",
    "tf.config.threading.set_intra_op_parallelism_threads(8)  # 设置单个操作内部的线程数\n",
    "tf.config.threading.set_inter_op_parallelism_threads(8)  # 设置多个操作之间的线程数\n",
    "\n",
    "\n",
    "# 加载MNIST数据集\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "# 数据预处理\n",
    "x_train = x_train / 255.0\n",
    "x_test = x_test / 255.0\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb3d13dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten (Flatten)           (None, 784)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               200960    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 242762 (948.29 KB)\n",
      "Trainable params: 242762 (948.29 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    Flatten(input_shape=(28, 28)),  # 将28x28的图像展平成784的向量\n",
    "    Dense(256, activation='relu'),  # 增加神经元数量\n",
    "    Dropout(0.5),  # 添加Dropout防止过拟合\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.5),  # 添加Dropout防止过拟合\n",
    "    Dense(64, activation='relu'),\n",
    "    Dense(10, activation='softmax')  # 输出层，10个节点，对应数字0-9\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', \n",
    "              loss='categorical_crossentropy', \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8fdc94e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "422/422 [==============================] - 2s 3ms/step - loss: 0.6037 - accuracy: 0.8074 - val_loss: 0.1494 - val_accuracy: 0.9550\n",
      "Epoch 2/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.2637 - accuracy: 0.9246 - val_loss: 0.1164 - val_accuracy: 0.9682\n",
      "Epoch 3/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.2104 - accuracy: 0.9384 - val_loss: 0.0991 - val_accuracy: 0.9708\n",
      "Epoch 4/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1792 - accuracy: 0.9471 - val_loss: 0.0882 - val_accuracy: 0.9750\n",
      "Epoch 5/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1625 - accuracy: 0.9517 - val_loss: 0.0911 - val_accuracy: 0.9737\n",
      "Epoch 6/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1462 - accuracy: 0.9567 - val_loss: 0.0812 - val_accuracy: 0.9775\n",
      "Epoch 7/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1338 - accuracy: 0.9602 - val_loss: 0.0756 - val_accuracy: 0.9793\n",
      "Epoch 8/15\n",
      "422/422 [==============================] - 2s 4ms/step - loss: 0.1247 - accuracy: 0.9633 - val_loss: 0.0705 - val_accuracy: 0.9802\n",
      "Epoch 9/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1198 - accuracy: 0.9647 - val_loss: 0.0712 - val_accuracy: 0.9827\n",
      "Epoch 10/15\n",
      "422/422 [==============================] - 2s 4ms/step - loss: 0.1138 - accuracy: 0.9656 - val_loss: 0.0649 - val_accuracy: 0.9817\n",
      "Epoch 11/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1095 - accuracy: 0.9673 - val_loss: 0.0678 - val_accuracy: 0.9818\n",
      "Epoch 12/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1028 - accuracy: 0.9691 - val_loss: 0.0709 - val_accuracy: 0.9807\n",
      "Epoch 13/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1007 - accuracy: 0.9698 - val_loss: 0.0637 - val_accuracy: 0.9827\n",
      "Epoch 14/15\n",
      "422/422 [==============================] - 2s 4ms/step - loss: 0.0954 - accuracy: 0.9715 - val_loss: 0.0657 - val_accuracy: 0.9825\n",
      "Epoch 15/15\n",
      "422/422 [==============================] - 3s 6ms/step - loss: 0.0949 - accuracy: 0.9711 - val_loss: 0.0686 - val_accuracy: 0.9803\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=15, batch_size=128, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6827b899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0725 - accuracy: 0.9798\n",
      "Test accuracy: 0.9797999858856201\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print(f\"Test accuracy: {test_acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "656a4dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VA0+Fdcbo1CIpCz7WVVt9dEFIt97OUgZTk+Y5EtZEKoGJ1AQKMxOxS6kRAxI9nFarCzwZpS1BZSEo80r5nD3VsbArDUGlD4duup9Kdw5fJ6Imp9EKyMxX4W5OEVJzHpT+LMKdCstpuUVQN8FlHROpBOZmuoBgLpNCYWoCc5kJFKYmUMhMoDCVwtZSVm2rj71V8w0+rQkDEhkqKQauHgTS/igPQllXdTM716RdB11LkH3X8tYg+266e5URETUBjVZAel6RPvSUhaA7pcuppeGnLpenylporBWm5SHGzAQKM2mF52WBRgqFzKQO25lAIZPq15kx4DR7DEhULicF+GYakPJb1ddMZLqJCyu3Btl15igyImpUJRot0vNUuFuh1eduTpHBcnpe3TolSyWAk40CzkoFXJXmcFYq4KJUlP40h4tSAQdrOQMMMSBRqcRfgf/MAAoydLM/ewdVaA3qqus8LW2+14qJqOXRagXcKyzWD0VPz1Mho0KH5Lu5ulagug5FN5VK9OHHRR98zPXPXZTmvDxFdcaA1NYJAnBiA3B4le5u9U69gAlfArYdxa6MiFqoEo1WPxRdF37KA1B6rgoZFcJQXUdkmZnowk/VVp/ylh8xRmNR68WA1Jap8oDvQoE/9+qWe08E/vIRL5kRUbVUJRpdC09pK09Z4KkYftLzVMgqUNVrtJadpQwO1nI42ijgZK0blu5obXgZzM5S1ixnW6bWiwGprcq4Aux8RTcqTWoGPBsB9Jut651IRK1aiUaLApUGuUVq5KtKkK8qQV6RGnlFZc9LcL+wGBmVWn+yC9V1PoaJVAJ7KxkcrXWjsBwrDEWvuM7eiv19qHliQGqL/twH7P0bUJwHWLsAL3+hm62aiJo1rVZAQXFJhSBjGGryi0qQV7o+v6jCdpXWPVBrHn6wGshMpKWtPbp5eCqGHcfSOXgcrRWwtZTxche1aAxIbYmmBPhpNXB8vW7ZYwjwUhTvHk/UxARBQJ6qBPfyi5FVoEJmfjGy8otxr+x5QTFyHqiRXzkAqUqMWofcVAprhSmsFWawkpvCSm4Ka4UprBSmUJqbVQ0/1nK0szCDhC3N1AYwILUVBZm6UWqJv+iWB4YCI98CTPgrQGQMD4o1yMxX4V5B1dCTlV+MzILy51n5xSjWaBt8LDMTSZVQYxB0ypbLnsvNKqzTPbeSm0JmyktbRDXht2NbcDsW+GYqkJsCmFkCL2wEeo4VuyqiZq24RIt7BcUGoScrvxiZlUJPVunrhcX1v2xlKTOBnZUctpYy2FvJYGcph62VDHaWMrSzkOlDjrWiPOBYyU0hN5WyFYeokTEgtWaCAMR+Bvz4hu4GsXadgQlfAY7eYldG1GwUqTW4lp6PP+/mIv5uHuJTc5GQmoesghpuqlwLmakU9pay0pAjh52VDPalAcjOUvfczkpWuiyHuYxzixE1VwxIrZX6AfDDIuD37bpl778AYzYDChtx6yISiSAIuJtThPjUXFy+m4fLd3MRn5qHxMyCGmdgNpFK9OHGrkLo0S3LK/2UwUpuypYdolaCAak1up+ku6R29zwgkQIjVgCDF3AIP7UZD4o1uJJWHoLKfuY8qH6YejsLM3R3tkF3Fxt4u1jD29ka7u0toDQ349w7RG0UA1Jrcy0G+HYW8OA+YGEHjNsKeD0ldlVEjUIQBNy+/wDxqXmIrxCGErMKqp2o0FQqgZeDVWkIskF3F2t0d7GBo7WcLT9EZIABqbXQaoFjHwI/rQEgAK59gJe/BNq5i10ZkVEUqEqQUNYqVNpXKP5uHvJqGPpubyXTtQg568KQt4s1OjtaQW7Kfj9E9HAMSK3Bg2xg71+BhB90y32DgVHvAWYKUcsiaghBEHDr3gNdp+nSEHQ5NRdJWYXVbm9mIkFnR2t0d7aucInMBg7W8iaunIhaEwakli7tT2DnFODeDcBEDjz3AdB3mthVEdVZem4R4m5l48LtHJy/rftZU18hJxu5vjWorM9QJwdL3qqCiIyOAaklu/gfYN88QF0IKN11twxx6yt2VUQ1ynmgxsXSIHS+NBSl5hZV2U5mIkVXZ6vSfkI26O5sjW7O1rCzYqsQETUNBqSWSKMG/rccOL1Zt9zpKV1nbEs7cesiqqBIrcEfd3JLg5AuDN3ILKiynVQCdHWyRu/HlOj9WDv4urdDVydrzvJMRKJiQGpp8lKBXdOB5JO65SdeB55aBkjZ8ZTEU6LR4kpaPi7czi5tHcrBlbQ8lFQzv1AHWwv0fkwJX/d26P1YO/RwtYGlnH+KiKh54V+lliT5FPBNMJCfCshtgBe3AN7PiV0VtTGCICApq1AfhC7czsalOzkoUle9t5i9lRw+jynh495O30JkaykToWoiovphQGoJBAE4/S/gf8sAbQng0B2YsB2w7yx2ZdQGpOUW6fsL1daJ2lpuil6lIagsFLkoFZxfiIhaJAak5q64APjvfODiLt1yj7HA8xsAuZW4dVGrpNUKOH87GyeuZ+H8Ld3lsrRcVZXtZKZSPO5iU6F1qB062Vty1mkiajUYkJqzrOvAzqlA+h+AxAR45p/AgL/yliFkVPmqEhy7moGYy+k4kpCOzHzDm7RKJUAXR2v4uJe1DrVDN2d2oiai1o0BqblK+BHY/SqgygEsHYGXPgM8B4tdFbUSyVmFiIlPw0/x6Th1IwtqTXlnaiu5KYZ0toefR3v4uLMTNRG1Tfyr19xoNcDPEcAv7+uWH+uvm9/IxkXcuqhFK9FocS45WxeKLqfjanq+wesedhYY4e2EEd0d0c/Tlq1DRNTmMSA1JyXFwI5JwLXDuuX+c4Fn1gCmHPVD9ZdTqMbRqxn46XIafr6SgezC8o7VJlIJ/D3aY2R3Jwzv7ohO9pbsTE1EVAEDUnNiKgPaewKm5kDQesBngtgVUQsiCAKuZxTgp/g0xFxOx29J96GpMA+R0twMT3VzwPDuTniyiwOUFmYiVktE1LwxIDU3gRFAvzmAo7fYlVALUFyixdmb9xBzOR0/xafhZqUbunZxtMLw7o4Y4e2Evh3awZT3LCMiqhMGpObGVMZwRLXKylfh54QMxMSn4ZcrmchXlehfMzORYEAnO4zwdsRwbyd0sLMQsVIiopaLAYmomRMEAfGpefgpPh0xl9Pw+61sCBXu4GFvJcNT3RwxorsjhnRxgBVHnBERPTL+JSVqhorUGpy8kYWfLqfjp/h0pGQ/MHi9h6uNrpWouxN6uyk5QSMRkZExIBE1A4Ig4GZWIX65koFfrmTgxPUsPFBr9K/LTaUY0tkew7s7Yri3I1yU5iJWS0TU+oneY3PTpk3w9PSEQqFAQEAAzpw5U+O2arUaq1evhpeXFxQKBXx8fHDgwAGDbVatWgWJRGLw8PY27NNTVFSEkJAQ2NnZwcrKCuPGjUNaWlqjnB9RTfJVJfjfH6kI33sRQ98/gqc++Bkr9/2BmPh0PFBr4GyjwOSADtga7I+4Fc9g6/R+mBLgwXBERNQERG1B2rlzJ8LCwrBlyxYEBAQgMjISgYGBSEhIgKOjY5Xtw8PDsX37dnz66afw9vbGwYMH8eKLL+LEiRPo06ePfrsePXrg8OHD+mVTU8PTXLhwIfbv349du3ZBqVQiNDQUY8eOxfHjxxvvZKnN02oF/HEnF79czcDRKxk4l3QfJRWG4ZuZSODn0R5Duzrgya4OeNzFhnMTERGJRCIIFbt7Nq2AgAD069cPGzduBABotVq4u7tj3rx5WLJkSZXtXV1dsWzZMoSEhOjXjRs3Dubm5ti+fTsAXQvS3r17ERcXV+0xc3Jy4ODggOjoaIwfPx4AEB8fj+7du+PkyZMYMGBAnWrPzc2FUqlETk4ObGxs6nPa1IZk5Knwa2kgOnY1E1kFhvc587SzwNCuDhjaxQEDvOzYwZqIqJHV9ftbtL/GxcXFiI2NxdKlS/XrpFIpRo4ciZMnT1b7HpVKBYVCYbDO3Nwcx44dM1h39epVuLq6QqFQYODAgYiIiECHDh0AALGxsVCr1Rg5cqR+e29vb3To0KHWgKRSqaBSld/VPDc3t34nTG1CcYkWvyXdwy9XMvHLlQz8edfw98RSZoKBXvZ4spsDnuziwGH4RETNlGgBKTMzExqNBk5OTgbrnZycEB8fX+17AgMDsW7dOgwdOhReXl6IiYnB7t27odGUd2YNCAjAZ599hm7duuHu3bt466238MQTT+DSpUuwtrZGamoqZDIZ2rVrV+W4qampNdYbERGBt956q+EnTK3WzcwCHC3tXH3yRhYKizUGr/d0s8HQLg4Y2tUBfTu0533OiIhagBbVnr9+/XrMmTMH3t7ekEgk8PLywowZM7Bt2zb9NqNGjdI/7927NwICAuDh4YFvvvkGs2bNavCxly5dirCwMP1ybm4u3N3dG7w/arnyVSU4cS0Tv1zNwC9XMpF8z3D2ansrOYZ2scfQrg4Y0sUe9lZykSolIqKGEi0g2dvbw8TEpMrosbS0NDg7O1f7HgcHB+zduxdFRUXIysqCq6srlixZgk6dOtV4nHbt2qFr1664du0aAMDZ2RnFxcXIzs42aEWq7bgAIJfLIZfzi64tqkvnan8PW11foq726O5sw3mJiIhaONECkkwmg5+fH2JiYjBmzBgAuk7aMTExCA0NrfW9CoUCbm5uUKvV+Pbbb/Hyyy/XuG1+fj6uX7+OqVOnAgD8/PxgZmaGmJgYjBs3DgCQkJCA5ORkDBw40DgnRy1eWefqX65k4NeHdK4e6GUHS3auJiJqVUT9qx4WFobg4GD4+/ujf//+iIyMREFBAWbMmAEAmDZtGtzc3BAREQEAOH36NFJSUuDr64uUlBSsWrUKWq0Wb7zxhn6fixYtQlBQEDw8PHDnzh2sXLkSJiYmmDRpEgBAqVRi1qxZCAsLg62tLWxsbDBv3jwMHDiwziPYqHUSBAG/XM3ExzFXEZt03+A1S5kJBnXWXTZj52oiotZP1IA0YcIEZGRkYMWKFUhNTYWvry8OHDig77idnJwMqbS8Q2tRURHCw8Nx48YNWFlZYfTo0fjyyy8NLpXdvn0bkyZNQlZWFhwcHDBkyBCcOnUKDg4O+m0++ugjSKVSjBs3DiqVCoGBgfjkk0+a7Lyp+Tl5PQsf/i8Bv1UIRmWdq5/s6oC+Hu1hZsLO1UREbYWo8yC1ZJwHqXWITbqPdYcScPxaFgDdLT2mDvDA3KGd4GijeMi7iYiopWn28yARielSSg7WHbqCn+LTAeg6Wk/q3wEhT3WGE4MREVGbx4BEbUpCah4+OnQFB/7QzXllIpVgfN/HMG9EZzzWnv2KiIhIhwGJ2oQbGflYH3MV+87fgSAAEgkwxtcN80d0gae9pdjlERFRM8OARK3arXuF+DjmKnb/ngJN6dxFo3s5Y8HIrujqZC1ydURE1FwxIFGrlJpThI1HrmLn2VtQa3TBaGR3Ryx8uit6uCpFro6IiJo7BiRqVTLyVNj883VsP52E4hItAOCJLvYIe7or+nRoL3J1RETUUjAgUauQXViMf/1yA58dv4kHat3NYvt72iLsma4Y0MlO5OqIiKilYUCiFi23SI2tvyZi27FE5KlKAAA+7u3w+tNd8UQXe0gkvCcaERHVHwMStUiFxSX47MRN/OvoDeQ8UAMAurvY4PWnu2JEd0cGIyIieiQMSNSiFKk1+Op0Mjb/fA2Z+bobyHZ2tMLCkV0xqqczpFIGIyIienQMSNQiFJdosfO3W9j401Wk5aoAAB52Flgwsgue93GDCYMREREZEQMSNWslGi12/56C9YevIiX7AQDAVanA30d0wTi/x3gDWSIiahQMSNQsabQCvr9wB5GHryIxswAA4GAtx7zhnTGhnzvkpiYiV0hERK0ZAxI1K4Ig4OAfaVh3KAFX0vIBALaWMvz1SS+8MsAD5jIGIyIianwMSNRsFJdosXLfJXx95hYAwEZhirlDO2H64I6wkvNXlYiImg6/dahZyMpX4a/bz+HMzXuQSIDXnvTCa096QWluJnZpRETUBjEgkegu383F7M9/Q0r2A1jLTfHxpD54yttR7LKIiKgNY0AiUR38IxULd8ahsFgDDzsLbA32R2dHa7HLIiKiNo4BiUQhCAI2HbmGD/53BQAwuLMdNk3ui3YWMpErIyIiYkAiETwo1uCNby/gv+fvAACCB3og/C+Pc04jIiJqNhiQqEml5hRhzhe/4WJKDkylEqx+oScmB3QQuywiIiIDDEjUZH5Pvo+5X8YiI0+F9hZm2PyKHwZ0shO7LCIioioYkKhJ7Pn9NhZ/exHFJVp0c7LG/wX7w93WQuyyiIiIqsWARI1KoxXw3sF4/OvoDQDAyO5OiJzoy4kfiYioWeO3FDWavCI15u+Iw0/x6QCAkKe88PrT3SCVSkSujIiIqHYMSNQokrIKMPvz33A1PR9yUyneG98bL/i6iV0WERFRnTAgkdGduJ6Jv311DtmFajjZyPHvqf7wcW8ndllERER1xoBERvXlqSS8te8PlGgF+DymxL+n+cPJRiF2WURERPXCgERGodZo8dZ//8D2U8kAgBd8XfHuuN5QmJmIXBkREVH9MSDRI7tfUIy/fXUOJ29kQSIB/hHYDX990gsSCTtjExFRy8SARI/kSloeZn1+FrfuPYClzATrJ/bByMedxC6LiIjokTAgUYMd/jMN83f8joJiDdxtzfF/0/qhm7O12GURERE9MgYkqjdBELDl6A28dzAeggAM6GSLT6b4wdZSJnZpRERERsGARPVSpNZgybcXsDfuDgBgSkAHrHq+B8xMpCJXRkREZDwMSFRnablFmPtlLM7fyoaJVIJVQY9j6kBPscsiIiIyOgYkqpMLt7Mx54vfkJarQjsLM3wyuS8GdbYXuywiIqJGwYBED/VdXAre+M8FqEq06Oxoha3B/vCwsxS7LCIiokYjeseRTZs2wdPTEwqFAgEBAThz5kyN26rVaqxevRpeXl5QKBTw8fHBgQMHDLaJiIhAv379YG1tDUdHR4wZMwYJCQkG2wwbNgwSicTg8dprrzXK+bVkWq2A9w/GY/6OOKhKtBju7Yg9fxvEcERERK2eqAFp586dCAsLw8qVK3Hu3Dn4+PggMDAQ6enp1W4fHh6Of/3rX9iwYQP+/PNPvPbaa3jxxRfx+++/67c5evQoQkJCcOrUKRw6dAhqtRrPPPMMCgoKDPY1Z84c3L17V/947733GvVcW5p8VQle3R6LTUeuAwBefbITPp3mD2uFmciVERERNT6JIAiCWAcPCAhAv379sHHjRgCAVquFu7s75s2bhyVLllTZ3tXVFcuWLUNISIh+3bhx42Bubo7t27dXe4yMjAw4Ojri6NGjGDp0KABdC5Kvry8iIyPrXKtKpYJKpdIv5+bmwt3dHTk5ObCxsanzflqCW/cKMfvz35CQlgeZqRTvjO2FsX0fE7ssIiKiR5abmwulUvnQ72/RWpCKi4sRGxuLkSNHlhcjlWLkyJE4efJkte9RqVRQKAxvfGpubo5jx47VeJycnBwAgK2trcH6r776Cvb29ujZsyeWLl2KwsLCWuuNiIiAUqnUP9zd3WvdvqW6lp6PFzYdR0JaHhys5dg5dwDDERERtTmiddLOzMyERqOBk5PhbSmcnJwQHx9f7XsCAwOxbt06DB06FF5eXoiJicHu3buh0Wiq3V6r1WLBggUYPHgwevbsqV8/efJkeHh4wNXVFRcuXMDixYuRkJCA3bt311jv0qVLERYWpl8ua0FqbXacSca9gmJ4O1sjakY/uCjNxS6JiIioybWoUWzr16/HnDlz4O3tDYlEAi8vL8yYMQPbtm2rdvuQkBBcunSpSgvT3Llz9c979eoFFxcXjBgxAtevX4eXl1e1+5LL5ZDL5cY7mWYqMVPXV+uVAR4MR0RE1GaJdonN3t4eJiYmSEtLM1iflpYGZ2fnat/j4OCAvXv3oqCgAElJSYiPj4eVlRU6depUZdvQ0FB8//33OHLkCB57rPZLRAEBAQCAa9euNfBsWo/ELF1A8uRINSIiasNEC0gymQx+fn6IiYnRr9NqtYiJicHAgQNrfa9CoYCbmxtKSkrw7bff4oUXXtC/JggCQkNDsWfPHvz000/o2LHjQ2uJi4sDALi4uDTsZFoJjVbArXu6vlie9hYiV0NERCQeUS+xhYWFITg4GP7+/ujfvz8iIyNRUFCAGTNmAACmTZsGNzc3REREAABOnz6NlJQU+Pr6IiUlBatWrYJWq8Ubb7yh32dISAiio6Px3XffwdraGqmpqQAApVIJc3NzXL9+HdHR0Rg9ejTs7Oxw4cIFLFy4EEOHDkXv3r2b/kNoRu5kP4BaI0BmKoUrL68REVEbJmpAmjBhAjIyMrBixQqkpqbC19cXBw4c0HfcTk5OhlRa3shVVFSE8PBw3LhxA1ZWVhg9ejS+/PJLtGvXTr/N5s2bAeiG8lcUFRWF6dOnQyaT4fDhw/ow5u7ujnHjxiE8PLzRz7e5K+t/1MHWAlKpRORqiIiIxCPqPEgtWV3nUWhJvjx5E8u/+wMjuzvh/4L9xS6HiIjI6Jr9PEjU/CRm6vofdWT/IyIiauMYkEjvZtkINnuOYCMioraNAYn0bmZyiD8RERHAgESlSjRa3LpfNsSfAYmIiNo2BiQCANzJLtIP8XexUTz8DURERK0YAxIBKJ9B24ND/ImIiBiQSCeJHbSJiIj0GJAIQPkkkR0ZkIiIiBiQSIcj2IiIiMoxIBEAICmrdASbHSeJJCIiYkAilGi0SL7HIf5ERERlGJAIKdkPUKIVIDeVwplD/ImIiBiQqLyDtocdh/gTEREBDEiEiv2PeHmNiIgIYEAicIg/ERFRZQxIhJtls2izBYmIiAgAAxKhwiU2ew7xJyIiAhiQ2rwSjRa3Sof48xIbERGRDgNSG3f7vm6Iv8JMCidrDvEnIiICGJDaPH3/I1tLDvEnIiIqxYDUxunvwcb+R0RERHoMSG3czSzeYoSIiKiyegckT09PrF69GsnJyY1RDzWxsjmQOEkkERFRuXoHpAULFmD37t3o1KkTnn76aezYsQMqlaoxaqMmkJTFgERERFRZgwJSXFwczpw5g+7du2PevHlwcXFBaGgozp071xg1UiNRa7S4df8BAA7xJyIiqqjBfZD69u2Ljz/+GHfu3MHKlSvxf//3f+jXrx98fX2xbds2CIJgzDqpEdy+/wCa0iH+jtZyscshIiJqNkwb+ka1Wo09e/YgKioKhw4dwoABAzBr1izcvn0bb775Jg4fPozo6Ghj1kpGdrPC5TUO8SciIipX74B07tw5REVF4euvv4ZUKsW0adPw0UcfwdvbW7/Niy++iH79+hm1UDK+m+ygTUREVK16B6R+/frh6aefxubNmzFmzBiYmZlV2aZjx46YOHGiUQqkxlM+BxIDEhERUUX1Dkg3btyAh4dHrdtYWloiKiqqwUVR00gsmwPJjpNEEhERVVTvTtrp6ek4ffp0lfWnT5/Gb7/9ZpSiqGnoh/izBYmIiMhAvQNSSEgIbt26VWV9SkoKQkJCjFIUNT61RovbHOJPRERUrXoHpD///BN9+/atsr5Pnz74888/jVIUNb5b9wqh0QowNzPhEH8iIqJK6h2Q5HI50tLSqqy/e/cuTE0bPGsANbGk0v5HHnYWkEg4xJ+IiKiiegekZ555BkuXLkVOTo5+XXZ2Nt588008/fTTRi2OGk/ZPdh4eY2IiKiqejf5fPDBBxg6dCg8PDzQp08fAEBcXBycnJzw5ZdfGr1Aahxlk0R6cA4kIiKiKuodkNzc3HDhwgV89dVXOH/+PMzNzTFjxgxMmjSp2jmRqHm6WXqJraM9h/gTERFV1qB7sVlaWmLu3LnYtGkTPvjgA0ybNq3B4WjTpk3w9PSEQqFAQEAAzpw5U+O2arUaq1evhpeXFxQKBXx8fHDgwIF677OoqAghISGws7ODlZUVxo0bV22/qtaMs2gTERHVrMG9qv/8808kJyejuLjYYP3zzz9f533s3LkTYWFh2LJlCwICAhAZGYnAwEAkJCTA0dGxyvbh4eHYvn07Pv30U3h7e+PgwYN48cUXceLECf3lvrrsc+HChdi/fz927doFpVKJ0NBQjB07FsePH2/ox9GiFJdocft+WQsSAxIREVEVQj1dv35d6N27tyCRSASpVCpIJBL9c6lUWq999e/fXwgJCdEvazQawdXVVYiIiKh2excXF2Hjxo0G68aOHStMmTKlzvvMzs4WzMzMhF27dum3uXz5sgBAOHnyZI21FhUVCTk5OfrHrVu3BABCTk5Ovc65ObiWnid4LP5e6L78R0Gr1YpdDhERUZPJycmp0/d3vS+xzZ8/Hx07dkR6ejosLCzwxx9/4JdffoG/vz9+/vnnOu+nuLgYsbGxGDlypH6dVCrFyJEjcfLkyWrfo1KpoFAoDNaZm5vj2LFjdd5nbGws1Gq1wTbe3t7o0KFDjccFgIiICCiVSv3D3d29zufa3CRV6KDNIf5ERERV1TsgnTx5EqtXr4a9vT2kUimkUimGDBmCiIgI/P3vf6/zfjIzM6HRaODk5GSw3snJCampqdW+JzAwEOvWrcPVq1eh1Wpx6NAh7N69G3fv3q3zPlNTUyGTydCuXbs6HxeAfmqDskd1s4m3FImZ7KBNRERUm3oHJI1GA2trawCAvb097ty5AwDw8PBAQkKCcaurZP369ejSpQu8vb0hk8kQGhqKGTNmQCptUF/zepHL5bCxsTF4tFRlHbQ5xJ+IiKh69U4WPXv2xPnz5wEAAQEBeO+993D8+HGsXr0anTp1qvN+7O3tYWJiUmX0WFpaGpydnat9j4ODA/bu3YuCggIkJSUhPj4eVlZW+uPWZZ/Ozs4oLi5GdnZ2nY/b2pTNgdSRAYmIiKha9Q5I4eHh0Gq1AIDVq1cjMTERTzzxBH744Qd8/PHHdd6PTCaDn58fYmJi9Ou0Wi1iYmIwcODAWt+rUCjg5uaGkpISfPvtt3jhhRfqvE8/Pz+YmZkZbJOQkIDk5OSHHre1KAtInhzBRkREVK16D/MPDAzUP+/cuTPi4+Nx7949tG/fvt4dfsPCwhAcHAx/f3/0798fkZGRKCgowIwZMwAA06ZNg5ubGyIiIgAAp0+fRkpKCnx9fZGSkoJVq1ZBq9XijTfeqPM+lUolZs2ahbCwMNja2sLGxgbz5s3DwIEDMWDAgPp+HC1OcYkWKfcfAAA82QeJiIioWvUKSGq1Gubm5oiLi0PPnj31621tbRt08AkTJiAjIwMrVqxAamoqfH19ceDAAX0n6+TkZIP+RUVFRQgPD8eNGzdgZWWF0aNH48svvzTocP2wfQLARx99BKlUinHjxkGlUiEwMBCffPJJg86hpUm+VwitAFjKTOBgJRe7HCIiomZJIgiCUJ83dOrUCXv27IGPj09j1dQi5ObmQqlUIicnp0V12I65nIZZn/+Gx11s8MP8J8Quh4iIqEnV9fu73n2Qli1bhjfffBP37t17pAJJHImlI9g4gzYREVHN6t0HaePGjbh27RpcXV3h4eEBS0vDL9pz584ZrTgyvpv6SSLZ/4iIiKgm9Q5IY8aMaYQyqKkkZekmieQINiIioprVOyCtXLmyMeqgJsJLbERERA/X+FNQU7OhKtHgTrZuiD8vsREREdWs3i1IUqm01vmONBrNIxVEjefWvQcc4k9ERFQH9Q5Ie/bsMVhWq9X4/fff8fnnn+Ott94yWmFkfGX3YPO0t6z3pJ5ERERtSb0DUtltPSoaP348evTogZ07d2LWrFlGKYyMj7cYISIiqhuj9UEaMGCAwf3NqPkp66Dtyf5HREREtTJKQHrw4AE+/vhjuLm5GWN31Ej0Q/zt2IJERERUm3pfYqt8U1pBEJCXlwcLCwts377dqMWRcXGIPxERUd3UOyB99NFHBgFJKpXCwcEBAQEBaN++vVGLI+MpUmtwJ6dsiD8DEhERUW3qHZCmT5/eCGVQY7t9vxCCAFjJTWFvJRO7HCIiomat3n2QoqKisGvXrirrd+3ahc8//9woRZHxJWaW3WLEgkP8iYiIHqLeASkiIgL29vZV1js6OmLt2rVGKYqMr2wOJF5eIyIierh6B6Tk5GR07NixynoPDw8kJycbpSgyvrI5kDoyIBERET1UvQOSo6MjLly4UGX9+fPnYWdnZ5SiyPg4SSQREVHd1TsgTZo0CX//+99x5MgRaDQaaDQa/PTTT5g/fz4mTpzYGDWSEdws7YPU0Z6TRBIRET1MvUexvf3227h58yZGjBgBU1Pd27VaLaZNm8Y+SM0Uh/gTERHVT70Dkkwmw86dO/HPf/4TcXFxMDc3R69eveDh4dEY9ZER3LqnG+JvLTeFnSWH+BMRET1MvQNSmS5duqBLly7GrIUaif4ebPaWHOJPRERUB/XugzRu3Di8++67Vda/9957eOmll4xSFBlXWQdtD96kloiIqE7qHZB++eUXjB49usr6UaNG4ZdffjFKUWRcN7PKOmiz/xEREVFd1Dsg5efnQyar2o/FzMwMubm5RimKjKtskkhPdtAmIiKqk3oHpF69emHnzp1V1u/YsQOPP/64UYoi49IHJA7xJyIiqpN6d9Jevnw5xo4di+vXr2P48OEAgJiYGERHR+M///mP0QukR6Mb4l8EgC1IREREdVXvgBQUFIS9e/di7dq1+M9//gNzc3P4+Pjgp59+gq2tbWPUSI8g+Z6u/5G1whS2HOJPRERUJw0a5v/cc8/hueeeAwDk5ubi66+/xqJFixAbGwuNRmPUAunRlA3x78gh/kRERHVW7z5IZX755RcEBwfD1dUVH374IYYPH45Tp04ZszYygrL+R5xBm4iIqO7q1YKUmpqKzz77DFu3bkVubi5efvllqFQq7N27lx20myn9EH/OgURERFRndW5BCgoKQrdu3XDhwgVERkbizp072LBhQ2PWRkZws8Is2kRERFQ3dW5B+vHHH/H3v/8df/3rX3mLkRakfBZtBiQiIqK6qnML0rFjx5CXlwc/Pz8EBARg48aNyMzMbMza6BEVqTW4WzrEn7NoExER1V2dA9KAAQPw6aef4u7du3j11VexY8cOuLq6QqvV4tChQ8jLy2vMOqkBkkr7H9koTNHewkzkaoiIiFqOeo9is7S0xMyZM3Hs2DFcvHgRr7/+Ot555x04Ojri+eefb4waqYE4xJ+IiKhhGjzMHwC6deuG9957D7dv38bXX39trJrISNj/iIiIqGEeKSCVMTExwZgxY7Bv3z5j7I6MJCmLI9iIiIgawigB6VFs2rQJnp6eUCgUCAgIwJkzZ2rdPjIyEt26dYO5uTnc3d2xcOFCFBUV6V/39PSERCKp8ggJCdFvM2zYsCqvv/baa412jmIpv8TGOZCIiIjqo0G3GjGWnTt3IiwsDFu2bEFAQAAiIyMRGBiIhIQEODo6Vtk+OjoaS5YswbZt2zBo0CBcuXIF06dPh0Qiwbp16wAAZ8+eNbjdyaVLl/D000/jpZdeMtjXnDlzsHr1av2yhUXrCxE3M3WdtHmJjYiIqH5EDUjr1q3DnDlzMGPGDADAli1bsH//fmzbtg1Lliypsv2JEycwePBgTJ48GYCutWjSpEk4ffq0fhsHBweD97zzzjvw8vLCk08+abDewsICzs7Oda5VpVJBpVLpl3Nzc+v8XjE8KNYgNbd0iD8DEhERUb2IdomtuLgYsbGxGDlyZHkxUilGjhyJkydPVvueQYMGITY2Vn8Z7saNG/jhhx8wevToGo+xfft2zJw5s8oorq+++gr29vbo2bMnli5disLCwlrrjYiIgFKp1D/c3d3rc7pNLume7vKa0twM7S1lIldDRETUsojWgpSZmQmNRgMnJyeD9U5OToiPj6/2PZMnT0ZmZiaGDBkCQRBQUlKC1157DW+++Wa12+/duxfZ2dmYPn16lf14eHjA1dUVFy5cwOLFi5GQkIDdu3fXWO/SpUsRFhamX87NzW3WIUl/ixHeg42IiKjeRL3EVl8///wz1q5di08++QQBAQG4du0a5s+fj7fffhvLly+vsv3WrVsxatQouLq6GqyfO3eu/nmvXr3g4uKCESNG4Pr16/Dy8qr22HK5HHK53Lgn1IgSS/sfcQQbERFR/YkWkOzt7WFiYoK0tDSD9WlpaTX2DVq+fDmmTp2K2bNnA9CFm4KCAsydOxfLli2DVFp+xTApKQmHDx+utVWoTEBAAADg2rVrNQaklkY/xJ/9j4iIiOpNtD5IMpkMfn5+iImJ0a/TarWIiYnBwIEDq31PYWGhQQgCdHMwAYAgCAbro6Ki4OjoiOeee+6htcTFxQEAXFxc6nMKzVrFWbSJiIiofkS9xBYWFobg4GD4+/ujf//+iIyMREFBgX5U27Rp0+Dm5oaIiAgAQFBQENatW4c+ffroL7EtX74cQUFB+qAE6IJWVFQUgoODYWpqeIrXr19HdHQ0Ro8eDTs7O1y4cAELFy7E0KFD0bt376Y7+UZWPos2+yARERHVl6gBacKECcjIyMCKFSuQmpoKX19fHDhwQN9xOzk52aDFKDw8HBKJBOHh4UhJSYGDgwOCgoKwZs0ag/0ePnwYycnJmDlzZpVjymQyHD58WB/G3N3dMW7cOISHhzfuyTahwuISpOXqpiRgCxIREVH9SYTK16aoTnJzc6FUKpGTkwMbGxuxyzFw+W4uRq3/Fe0szBC34hmxyyEiImo26vr9LfqtRsj4yob4cwZtIiKihmFAaoVuZumG+Hdk/yMiIqIGYUBqhfSTRLL/ERERUYMwILVCiZwDiYiI6JEwILVCbEEiIiJ6NAxIrUxhcQnS80qH+LMFiYiIqEEYkFqZm6X3YGtvYQalhZnI1RAREbVMDEitTPkM2mw9IiIiaigGpFamLCBxBm0iIqKGY0BqZfQdtNmCRERE1GAMSK1MWR8kT3tOEklERNRQDEitzE3OgURERPTIGJBakQJV+RB/BiQiIqKGY0BqRcpajzjEn4iI6NEwILUi5f2P2HpERET0KBiQWhH9EH9eXiMiInokDEitCO/BRkREZBwMSK1I+SzaHOJPRET0KBiQWpGbWbo+SJxFm4iI6NEwILUS+aoSZJQO8ed92IiIiB4NA1IrUdb/yNZSBqU5h/gTERE9CgakVqJ8Bm32PyIiInpUDEitRFIW50AiIiIyFgakViIxk3MgERERGQsDUitR1gfJgy1IREREj4wBqZXQD/FnCxIREdEjY0BqBfKK1MjMLx3ib89O2kRERI+KAakVKOugbWcpg42CQ/yJiIgeFQNSK6Af4s/+R0REREbBgNQK6G9Sy/5HRERERsGA1AokZpbOgcRJIomIiIyCAakV4CU2IiIi42JAagWSSgNSRwYkIiIio2BAauF0Q/yLAQAevMRGRERkFAxILdzN0v5H9lYyWHOIPxERkVEwILVw+v5HHMFGRERkNAxILZx+iD/7HxERERmN6AFp06ZN8PT0hEKhQEBAAM6cOVPr9pGRkejWrRvMzc3h7u6OhQsXoqioSP/6qlWrIJFIDB7e3t4G+ygqKkJISAjs7OxgZWWFcePGIS0trVHOr7El6luQ2P+IiIjIWEQNSDt37kRYWBhWrlyJc+fOwcfHB4GBgUhPT692++joaCxZsgQrV67E5cuXsXXrVuzcuRNvvvmmwXY9evTA3bt39Y9jx44ZvL5w4UL897//xa5du3D06FHcuXMHY8eObbTzbExsQSIiIjI+UzEPvm7dOsyZMwczZswAAGzZsgX79+/Htm3bsGTJkirbnzhxAoMHD8bkyZMBAJ6enpg0aRJOnz5tsJ2pqSmcnZ2rPWZOTg62bt2K6OhoDB8+HAAQFRWF7t2749SpUxgwYEC171OpVFCpVPrl3Nzc+p9wIyi7Dxv7IBERERmPaC1IxcXFiI2NxciRI8uLkUoxcuRInDx5str3DBo0CLGxsfrLcDdu3MAPP/yA0aNHG2x39epVuLq6olOnTpgyZQqSk5P1r8XGxkKtVhsc19vbGx06dKjxuAAQEREBpVKpf7i7uzfovI0pt0iNrALdEH+2IBERERmPaAEpMzMTGo0GTk5OBuudnJyQmppa7XsmT56M1atXY8iQITAzM4OXlxeGDRtmcIktICAAn332GQ4cOIDNmzcjMTERTzzxBPLy8gAAqampkMlkaNeuXZ2PCwBLly5FTk6O/nHr1q0GnrnxlF1es7eSw0ouamMgERFRqyJ6J+36+Pnnn7F27Vp88sknOHfuHHbv3o39+/fj7bff1m8zatQovPTSS+jduzcCAwPxww8/IDs7G998880jHVsul8PGxsbgIbabpZfXOtqzgzYREZExidbsYG9vDxMTkyqjx9LS0mrsP7R8+XJMnToVs2fPBgD06tULBQUFmDt3LpYtWwaptGrea9euHbp27Ypr164BAJydnVFcXIzs7GyDVqTajttc6Ttos/8RERGRUYnWgiSTyeDn54eYmBj9Oq1Wi5iYGAwcOLDa9xQWFlYJQSYmJgAAQRCqfU9+fj6uX78OFxcXAICfnx/MzMwMjpuQkIDk5OQaj9tccQQbERFR4xC140pYWBiCg4Ph7++P/v37IzIyEgUFBfpRbdOmTYObmxsiIiIAAEFBQVi3bh369OmDgIAAXLt2DcuXL0dQUJA+KC1atAhBQUHw8PDAnTt3sHLlSpiYmGDSpEkAAKVSiVmzZiEsLAy2trawsbHBvHnzMHDgwBpHsDVXnEWbiIiocYgakCZMmICMjAysWLECqamp8PX1xYEDB/Qdt5OTkw1ajMLDwyGRSBAeHo6UlBQ4ODggKCgIa9as0W9z+/ZtTJo0CVlZWXBwcMCQIUNw6tQpODg46Lf56KOPIJVKMW7cOKhUKgQGBuKTTz5puhM3krI+SJ7sg0RERGRUEqGma1NUq9zcXCiVSuTk5IjSYTvngRo+b/0PAHDprUCOYiMiIqqDun5/t6hRbFSurP+RgzWH+BMRERkbA1ILVdb/qCP7HxERERkdA1ILdTNT1//IgzepJSIiMjoGpBZKP4KNQ/yJiIiMjgGphdJfYmNAIiIiMjoGpBaKs2gTERE1HgakFiinUI37hWoA7INERETUGBiQWqCyy2uO1nJYcog/ERGR0TEgtUDsoE1ERNS4GJBaoER9/yNeXiMiImoMDEgtkL6DNluQiIiIGgUDUgtUdpNazqJNRETUOBiQWiD2QSIiImpcDEgtTHZhMbI5xJ+IiKhRMSC1MGWX15xs5LCQcYg/ERFRY2BAamE4gzYREVHjY0BqYRIZkIiIiBodA1ILww7aREREjY8BqYXRD/G3ZwdtIiKixsKA1MKU9UHy4CU2IiKiRsOA1ILcLyhGzgPdEH/2QSIiImo8DEgtSFn/I2cbBcxlJiJXQ0RE1HoxILUg5R202f+IiIioMTEgtSCJmboO2ry8RkRE1LgYkFqQJA7xJyIiahIMSC0IZ9EmIiJqGgxILYQgCOWzaLMPEhERUaNiQGoh7heqkVtUAgDwsGULEhERUWNiQGohykawuSg5xJ+IiKixMSC1EOUzaPPyGhERUWNjQGohygJSR45gIyIianQMSC1E2U1qOYKNiIio8TEgtRA3OQcSERFRk2FAagEMhvizBYmIiKjRMSC1APcL1cgrG+LPTtpERESNjgGpBShrPXJVKqAw4xB/IiKixsaA1AKUD/Hn5TUiIqKmYCp2AZs2bcL777+P1NRU+Pj4YMOGDejfv3+N20dGRmLz5s1ITk6Gvb09xo8fj4iICCgUCgBAREQEdu/ejfj4eJibm2PQoEF499130a1bN/0+hg0bhqNHjxrs99VXX8WWLVsa5yQfETtoE5GxaTQaqNVqscsgMjozMzOYmDz61RZRA9LOnTsRFhaGLVu2ICAgAJGRkQgMDERCQgIcHR2rbB8dHY0lS5Zg27ZtGDRoEK5cuYLp06dDIpFg3bp1AICjR48iJCQE/fr1Q0lJCd58800888wz+PPPP2FpWR4w5syZg9WrV+uXLSyab9+esiH+HXkPNiJ6RIIgIDU1FdnZ2WKXQtRo2rVrB2dnZ0gkkgbvQ9SAtG7dOsyZMwczZswAAGzZsgX79+/Htm3bsGTJkirbnzhxAoMHD8bkyZMBAJ6enpg0aRJOnz6t3+bAgQMG7/nss8/g6OiI2NhYDB06VL/ewsICzs7Oda5VpVJBpVLpl3Nzc+v83kfFS2xEZCxl4cjR0REWFhaP9AVC1NwIgoDCwkKkp6cDAFxcXBq8L9ECUnFxMWJjY7F06VL9OqlUipEjR+LkyZPVvmfQoEHYvn07zpw5g/79++PGjRv44YcfMHXq1BqPk5OTAwCwtbU1WP/VV19h+/btcHZ2RlBQEJYvX15rK1JERATeeuut+pyiUQiCwFm0icgoNBqNPhzZ2dmJXQ5RozA3NwcApKenw9HRscGX20QLSJmZmdBoNHBycjJY7+TkhPj4+GrfM3nyZGRmZmLIkCEQBAElJSV47bXX8Oabb1a7vVarxYIFCzB48GD07NnTYD8eHh5wdXXFhQsXsHjxYiQkJGD37t011rt06VKEhYXpl3Nzc+Hu7l6fU26QewXFyFOVQCIBOtjyEhsRNVxZn6Pm3KWAyBjKfsfVanXLC0gN8fPPP2Pt2rX45JNPEBAQgGvXrmH+/Pl4++23sXz58irbh4SE4NKlSzh27JjB+rlz5+qf9+rVCy4uLhgxYgSuX78OLy+vao8tl8shl8uNe0J1UNZB21VpziH+RGQUvKxGrZ0xfsdFC0j29vYwMTFBWlqawfq0tLQa+wYtX74cU6dOxezZswHowk1BQQHmzp2LZcuWQSotn7UgNDQU33//PX755Rc89thjtdYSEBAAALh27VqNAUksiZm6DtqcIJKIiKjpiDYPkkwmg5+fH2JiYvTrtFotYmJiMHDgwGrfU1hYaBCCAOibzgRB0P8MDQ3Fnj178NNPP6Fjx44PrSUuLg7Ao3Xmaixl/Y84xJ+IyLg8PT0RGRlZ5+1//vlnSCQSjgBsI0S9xBYWFobg4GD4+/ujf//+iIyMREFBgX5U27Rp0+Dm5oaIiAgAQFBQENatW4c+ffroL7EtX74cQUFB+qAUEhKC6OhofPfdd7C2tkZqaioAQKlUwtzcHNevX0d0dDRGjx4NOzs7XLhwAQsXLsTQoUPRu3dvcT6IWpRdYuvIEWxE1EY97HLJypUrsWrVqnrv9+zZswbTvzzMoEGDcPfuXSiVynofq6G8vb2RmJiIpKSkeo28pkcnakCaMGECMjIysGLFCqSmpsLX1xcHDhzQd9xOTk42aDEKDw+HRCJBeHg4UlJS4ODggKCgIKxZs0a/zebNmwHoJoOsKCoqCtOnT4dMJsPhw4f1Yczd3R3jxo1DeHh4459wA5QFJF5iI6K26u7du/rnO3fuxIoVK5CQkKBfZ2VlpX8uCAI0Gg1MTR/+9ebg4FCvOmQyWZOGlGPHjuHBgwcYP348Pv/8cyxevLjJjl0dtVoNMzMzUWtoUgI1SE5OjgBAyMnJabRjaLVaoceKA4LH4u+FK6m5jXYcImobHjx4IPz555/CgwcP9Ou0Wq1QoFI3+UOr1TboHKKiogSlUqlfPnLkiABA+OGHH4S+ffsKZmZmwpEjR4Rr164Jzz//vODo6ChYWloK/v7+wqFDhwz25eHhIXz00Uf6ZQDCp59+KowZM0YwNzcXOnfuLHz33XdVjnX//n2DWg4cOCB4e3sLlpaWQmBgoHDnzh39e9RqtTBv3jxBqVQKtra2whtvvCFMmzZNeOGFFx56rtOnTxeWLFki/Pjjj0LXrl2rvH7r1i1h4sSJQvv27QULCwvBz89POHXqlP71ffv2Cf7+/oJcLhfs7OyEMWPGGJzrnj17DPanVCqFqKgoQRAEITExUQAg7NixQxg6dKggl8uFqKgoITMzU5g4caLg6uoqmJubCz179hSio6MN9qPRaIR3331X8PLyEmQymeDu7i7885//FARBEJ566ikhJCTEYPv09HTBzMxMOHz48EM/k7qq7ne9TF2/v1vUKLa2JqugGPmlQ/zdOcSfiBrBA7UGj6842OTH/XN1ICxkxvsKWrJkCT744AN06tQJ7du3x61btzB69GisWbMGcrkcX3zxBYKCgpCQkIAOHTrUuJ+33noL7733Ht5//31s2LABU6ZMQVJSUpW59MoUFhbigw8+wJdffgmpVIpXXnkFixYtwldffQUAePfdd/HVV18hKioK3bt3x/r167F371489dRTtZ5PXl4edu3ahdOnT8Pb2xs5OTn49ddf8cQTTwAA8vPz8eSTT8LNzQ379u2Ds7Mzzp07B61WCwDYv38/XnzxRSxbtgxffPEFiouL8cMPPzToc/3www/Rp08fKBQKFBUVwc/PD4sXL4aNjQ3279+PqVOnwsvLS3+bsKVLl+LTTz/FRx99hCFDhuDu3bv66Xtmz56N0NBQfPjhh/qR4du3b4ebmxuGDx9e7/oaEwNSM1bWQZtD/ImIard69Wo8/fTT+mVbW1v4+Pjol99++23s2bMH+/btQ2hoaI37mT59OiZNmgQAWLt2LT7++GOcOXMGzz77bLXbq9VqbNmyRT8COjQ01OA2Vhs2bMDSpUvx4osvAgA2btxYp6CyY8cOdOnSBT169AAATJw4EVu3btUHpOjoaGRkZODs2bP68Na5c2f9+9esWYOJEycaTHBc8fOoqwULFmDs2LEG6xYtWqR/Pm/ePBw8eBDffPMN+vfvj7y8PKxfvx4bN25EcHAwAMDLywtDhgwBAIwdOxahoaH47rvv8PLLLwPQ3fGi7LZhzQkDUjOWqB/BxtYjImoc5mYm+HN1oCjHNSZ/f3+D5fz8fKxatQr79+/H3bt3UVJSggcPHiA5ObnW/VQcrGNpaQkbGxv9bSuqY2FhYTA9jIuLi377nJwcpKWlGdyA3cTEBH5+fvqWnpps27YNr7zyin75lVdewZNPPokNGzbA2toacXFx6NOnT40tW3FxcZgzZ06tx6iLyp+rRqPB2rVr8c033yAlJQXFxcVQqVT6iRkvX74MlUqFESNGVLs/hUKBqVOnYtu2bXj55Zdx7tw5XLp0Cfv27XvkWo2NAakZSyq9Sa0nR7ARUSORSCRGvdQllsqj0RYtWoRDhw7hgw8+QOfOnWFubo7x48ejuLi41v1U7oQskUhqDTPVbS+UTjvTUH/++SdOnTqFM2fOGHTM1mg02LFjB+bMmaO/nUZNHvZ6dXWWzbReUeXP9f3338f69esRGRmJXr16wdLSEgsWLNB/rg87LqC7zObr64vbt28jKioKw4cPh4eHx0Pf19REmweJHi4xi/dgIyJqiOPHj2P69Ol48cUX0atXLzg7O+PmzZtNWoNSqYSTkxPOnj2rX6fRaHDu3Lla37d161YMHToU58+fR1xcnP4RFhaGrVu3AtC1dMXFxeHevXvV7qN3794G8wxW5uDgYDA68OrVqygsLHzoOR0/fhwvvPACXnnlFfj4+KBTp064cuWK/vUuXbrA3Ny81mP36tUL/v7++PTTTxEdHY2ZM2c+9LhiYEBqxsr6IHmwBYmIqF66dOmC3bt3Iy4uDufPn8fkyZMfelmrMcybNw8RERH47rvvkJCQgPnz5+P+/fs19rdRq9X48ssvMWnSJPTs2dPgMXv2bJw+fRp//PEHJk2aBGdnZ4wZMwbHjx/HjRs38O233+pv9r5y5Up8/fXXWLlyJS5fvoyLFy/i3Xff1R9n+PDh2LhxI37//Xf89ttveO211+o0hL9Lly44dOgQTpw4gcuXL+PVV181uCOGQqHA4sWL8cYbb+CLL77A9evXcerUKX2wKzN79my88847EARB3z+ruWFAaqYEQdAHpI7sg0REVC/r1q1D+/btMWjQIAQFBSEwMBB9+/Zt8joWL16MSZMmYdq0aRg4cCCsrKwQGBgIhUJR7fb79u1DVlZWtaGhe/fu6N69O7Zu3QqZTIb//e9/cHR0xOjRo9GrVy+88847+kmThw0bhl27dmHfvn3w9fXF8OHDcebMGf2+PvzwQ7i7u+OJJ57A5MmTsWjRojrdxDg8PBx9+/ZFYGAghg0bpg9pFS1fvhyvv/46VqxYge7du2PChAlV+nFNmjQJpqammDRpUo2fhdgkwqNeLG2jcnNzoVQqkZOTAxsbG6PvPyNPhX5rDkMiAeLffhZyU45iI6JHU1RUhMTERHTs2LHZfim1dlqtFt27d8fLL7+Mt99+W+xyRHPz5k14eXnh7NmzjRJca/tdr+v3d8vvmddKlc2g7ao0ZzgiImqhkpKS8L///Q9PPvkkVCoVNm7ciMTEREyePFns0kShVquRlZWF8PBwDBgwQJRWvbriJbZmKjGTHbSJiFo6qVSKzz77DP369cPgwYNx8eJFHD58GN27dxe7NFEcP34cLi4uOHv2LLZs2SJ2ObViC1IzlZTFOZCIiFo6d3d3HD9+XOwymo1hw4Y98jQITYUtSM3UzUzOgURERCQWBqRmSj+LNgMSERFRk2NAaoYEQdB30vZkHyQiIqImx4DUDGXkq1BYrIFUArjbPnzadiIiIjIuBqRmqKz/kWs7DvEnIiISAwNSM3STQ/yJiIhExYDUDOn7H7GDNhGR0QwbNgwLFizQL3t6eiIyMrLW90gkEuzdu/eRj22s/VDTYUBqhsoCkocd50AiIgoKCsKzzz5b7Wu//vorJBIJLly4UO/9nj17FnPnzn3U8gysWrUKvr6+VdbfvXsXo0aNMuqxavLgwQPY2trC3t4eKpWqSY7ZGjEgNUOJpX2QeImNiAiYNWsWDh06hNu3b1d5LSoqCv7+/ujdu3e99+vg4FCnG7Qag7OzM+RyeZMc69tvv0WPHj3g7e0tequVIAgoKSkRtYaGYkBqZgRBqDCLNgMSETUyQQCKC5r+UY/ZlP/yl7/AwcEBn332mcH6/Px87Nq1C7NmzUJWVhYmTZoENzc3WFhYoFevXvj6669r3W/lS2xXr17F0KFDoVAo8Pjjj+PQoUNV3rN48WJ07doVFhYW6NSpE5YvXw61Wg0A+Oyzz/DWW2/h/PnzkEgkkEgk+porX2K7ePEihg8fDnNzc9jZ2WHu3LnIz8/Xvz59+nSMGTMGH3zwAVxcXGBnZ4eQkBD9sWqzdetWvPLKK3jllVewdevWKq//8ccf+Mtf/gIbGxtYW1vjiSeewPXr1/Wvb9u2DT169IBcLoeLiwtCQ0MB6G4wK5FIEBcXp982OzsbEokEP//8MwDg559/hkQiwY8//gg/Pz/I5XIcO3YM169fxwsvvAAnJydYWVmhX79+OHz4sEFdKpUKixcvhru7O+RyOTp37oytW7dCEAR07twZH3zwgcH2cXFxkEgkuHbt2kM/k4bgrUaamYy8CkP82/MSGxE1MnUhsNa16Y/75h1AVrd/BJqammLatGn47LPPsGzZMkgkEgDArl27oNFoMGnSJOTn58PPzw+LFy+GjY0N9u/fj6lTp8LLywv9+/d/6DG0Wi3Gjh0LJycnnD59Gjk5OQb9lcpYW1vjs88+g6urKy5evIg5c+bA2toab7zxBiZMmIBLly7hwIED+i9/pVJZZR8FBQUIDAzEwIEDcfbsWaSnp2P27NkIDQ01CIFHjhyBi4sLjhw5gmvXrmHChAnw9fXFnDlzajyP69ev4+TJk9i9ezcEQcDChQuRlJQEDw8PAEBKSgqGDh2KYcOG4aeffoKNjQ2OHz+ub+XZvHkzwsLC8M4772DUqFHIyclp0K1SlixZgg8++ACdOnVC+/btcevWLYwePRpr1qyBXC7HF198gaCgICQkJKBDhw4AgGnTpuHkyZP4+OOP4ePjg8TERGRmZkIikWDmzJmIiorCokWL9MeIiorC0KFD0blz53rXVxcMSM1M2Qzabu3NITNlAx8REQDMnDkT77//Po4ePYphw4YB0H1Bjhs3DkqlEkql0uDLc968eTh48CC++eabOgWkw4cPIz4+HgcPHoSrqy4wrl27tkq/ofDwcP1zT09PLFq0CDt27MAbb7wBc3NzWFlZwdTUFM7OzjUeKzo6GkVFRfjiiy9gaakLiRs3bkRQUBDeffddODk5AQDat2+PjRs3wsTEBN7e3njuuecQExNTa0Datm0bRo0ahfbt2wMAAgMDERUVhVWrVgEANm3aBKVSiR07dsDMzAwA0LVrV/37//nPf+L111/H/Pnz9ev69ev30M+vstWrV+Ppp5/WL9va2sLHx0e//Pbbb2PPnj3Yt28fQkNDceXKFXzzzTc4dOgQRo4cCQDo1KmTfvvp06djxYoVOHPmDPr37w+1Wo3o6OgqrUrGxIDUzHAEGxE1KTMLXWuOGMetB29vbwwaNAjbtm3DsGHDcO3aNfz6669YvXo1AECj0WDt2rX45ptvkJKSguLiYqhUqjr3Mbp8+TLc3d314QgABg4cWGW7nTt34uOPP8b169eRn5+PkpIS2NjY1OtcLl++DB8fH304AoDBgwdDq9UiISFBH5B69OgBE5PyufBcXFxw8eLFGver0Wjw+eefY/369fp1r7zyChYtWoQVK1ZAKpUiLi4OTzzxhD4cVZSeno47d+5gxIgR9Tqf6vj7+xss5+fnY9WqVdi/fz/u3r2LkpISPHjwAMnJyQB0l8tMTEzw5JNPVrs/V1dXPPfcc9i2bRv69++P//73v1CpVHjppZceudaasImimbmZxQ7aRNSEJBLdpa6mfpReJquPWbNm4dtvv0VeXh6ioqLg5eWl/0J9//33sX79eixevBhHjhxBXFwcAgMDUVxcbLSP6uTJk5gyZQpGjx6N77//Hr///juWLVtm1GNUVDnESCQSaLXaGrc/ePAgUlJSMGHCBJiamsLU1BQTJ05EUlISYmJiAADm5jXfnaG21wBAKtVFBqFC/7Ga+kRVDH8AsGjRIuzZswdr167Fr7/+iri4OPTq1Uv/2T3s2AAwe/Zs7NixAw8ePEBUVBQmTJjQqJ3sGZCambJJIj3YgkREZODll1+GVCpFdHQ0vvjiC8ycOVPfH+n48eN44YUX8Morr8DHxwedOnXClStX6rzv7t2749atW7h7965+3alTpwy2OXHiBDw8PLBs2TL4+/ujS5cuSEpKMthGJpNBo9E89Fjnz59HQUGBft3x48chlUrRrVu3Otdc2datWzFx4kTExcUZPCZOnKjvrN27d2/8+uuv1QYba2treHp66sNUZQ4ODgBg8BlV7LBdm+PHj2P69Ol48cUX0atXLzg7O+PmzZv613v16gWtVoujR4/WuI/Ro0fD0tISmzdvxoEDBzBz5sw6HbuhGJCaGbVGCxOpBB3t2UGbiKgiKysrTJgwAUuXLsXdu3cxffp0/WtdunTBoUOHcOLECVy+fBmvvvoq0tLS6rzvkSNHomvXrggODsb58+fx66+/YtmyZQbbdOnSBcnJydixYweuX7+Ojz/+GHv27DHYxtPTE4mJiYiLi0NmZma18xBNmTIFCoUCwcHBuHTpEo4cOYJ58+Zh6tSp+str9ZWRkYH//ve/CA4ORs+ePQ0e06ZNw969e3Hv3j2EhoYiNzcXEydOxG+//YarV6/iyy+/REJCAgDdPE4ffvghPv74Y1y9ehXnzp3Dhg0bAOhaeQYMGIB33nkHly9fxtGjRw36ZNWmS5cu2L17N+Li4nD+/HlMnjzZoDXM09MTwcHBmDlzJvbu3YvExET8/PPP+Oabb/TbmJiYYPr06Vi6dCm6dOlS7SVQY2JAamb+L7gf4t9+Fk90cRC7FCKiZmfWrFm4f/8+AgMDDfoLhYeHo2/fvggMDMSwYcPg7OyMMWPG1Hm/UqkUe/bswYMHD9C/f3/Mnj0ba9asMdjm+eefx8KFCxEaGgpfX1+cOHECy5cvN9hm3LhxePbZZ/HUU0/BwcGh2qkGLCwscPDgQdy7dw/9+vXD+PHjMWLECGzcuLF+H0YFZR2+q+s/NGLECJibm2P79u2ws7PDTz/9hPz8fDz55JPw8/PDp59+qr+cFxwcjMjISHzyySfo0aMH/vKXv+Dq1av6fW3btg0lJSXw8/PDggUL8M9//rNO9a1btw7t27fHoEGDEBQUhMDAQPTt29dgm82bN2P8+PH429/+Bm9vb8yZM8eglQ3Q/fcvLi7GjBkz6vsR1ZtEEOoxGQXp5ebmQqlUIicnp94d9IiIxFBUVITExER07NgRCoVC7HKI6u3XX3/FiBEjcOvWrVpb22r7Xa/r9zdHsREREVGzplKpkJGRgVWrVuGll15q8KXI+uAlNiIiImrWvv76a3h4eCA7OxvvvfdekxyTAYmIiIiatenTp0Oj0SA2NhZubm5NckwGJCIiIqJKGJCIiNoYjs2h1s4Yv+MMSEREbUTZUO7CwkKRKyFqXGW/49XdUqWuOIqNiKiNMDExQbt27ZCeng5ANx+PpAG3/CBqrgRBQGFhIdLT09GuXTuDe9nVl+gBadOmTXj//feRmpoKHx8fbNiwodY7L0dGRmLz5s1ITk6Gvb09xo8fj4iICIN5Dh62z6KiIrz++uvYsWMHVCoVAgMD8cknnzTJsEEiIjGV3WW+LCQRtUbt2rXT/643lKgBaefOnQgLC8OWLVsQEBCAyMhIBAYGIiEhAY6OjlW2j46OxpIlS7Bt2zYMGjQIV65cwfTp0yGRSLBu3bo673PhwoXYv38/du3aBaVSidDQUIwdOxbHjx9v0vMnImpqEokELi4ucHR0rPFGo0QtmZmZ2SO1HJURdSbtgIAA9OvXTz+9ularhbu7O+bNm4clS5ZU2T40NBSXL182uJHe66+/jtOnT+PYsWN12mdOTg4cHBwQHR2N8ePHAwDi4+PRvXt3nDx5EgMGDKhT7ZxJm4iIqOWp6/e3aJ20i4uLERsbi5EjR5YXI5Vi5MiROHnyZLXvGTRoEGJjY3HmzBkAwI0bN/DDDz9g9OjRdd5nbGws1Gq1wTbe3t7o0KFDjccFdLN45ubmGjyIiIiodRLtEltmZiY0Gk2Vfj9OTk6Ij4+v9j2TJ09GZmYmhgwZAkEQUFJSgtdeew1vvvlmnfeZmpoKmUyGdu3aVdkmNTW1xnojIiLw1ltv1fc0iYiIqAVqUcP8f/75Z6xduxaffPIJzp07h927d2P//v14++23G/3YS5cuRU5Ojv5x69atRj8mERERiUO0FiR7e3uYmJggLS3NYH1aWlqNPc+XL1+OqVOnYvbs2QCAXr16oaCgAHPnzsWyZcvqtE9nZ2cUFxcjOzvboBWptuMCgFwuh1wu1y+Xdd3ipTYiIqKWo+x7+2FdsEULSDKZDH5+foiJicGYMWMA6DpUx8TEIDQ0tNr3FBYWQio1bPQq66kuCEKd9unn5wczMzPExMRg3LhxAICEhAQkJydj4MCBda4/Ly8PAODu7l7n9xAREVHzkJeXB6VSWeProg7zDwsLQ3BwMPz9/dG/f39ERkaioKAAM2bMAABMmzYNbm5uiIiIAAAEBQVh3bp16NOnDwICAnDt2jUsX74cQUFB+qD0sH0qlUrMmjULYWFhsLW1hY2NDebNm4eBAwfWeQQbALi6uuLWrVuwtrY26kRrubm5cHd3x61bt9rs6Li2/hm09fMH+Bnw/Nv2+QP8DBrz/AVBQF5eHlxdXWvdTtSANGHCBGRkZGDFihVITU2Fr68vDhw4oO9knZycbNBiFB4eDolEgvDwcKSkpMDBwQFBQUFYs2ZNnfcJAB999BGkUinGjRtnMFFkfUilUjz22GOP+AnUzMbGpk3+T1FRW/8M2vr5A/wMeP5t+/wBfgaNdf61tRyVEXUeJKqK8yvxM2jr5w/wM+D5t+3zB/gZNIfzb1Gj2IiIiIiaAgNSMyOXy7Fy5UqDEXNtTVv/DNr6+QP8DHj+bfv8AX4GzeH8eYmNiIiIqBK2IBERERFVwoBEREREVAkDEhEREVElDEhERERElTAgNTObNm2Cp6cnFAoFAgICcObMGbFLahIRERHo168frK2t4ejoiDFjxiAhIUHsskTzzjvvQCKRYMGCBWKX0qRSUlLwyiuvwM7ODubm5ujVqxd+++03sctqMhqNBsuXL0fHjh1hbm4OLy8vvP322w+9Z1RL9csvvyAoKAiurq6QSCTYu3evweuCIGDFihVwcXGBubk5Ro4ciatXr4pTbCOp7TNQq9VYvHgxevXqBUtLS7i6umLatGm4c+eOeAUb2cN+Byp67bXXIJFIEBkZ2SS1MSA1Izt37kRYWBhWrlyJc+fOwcfHB4GBgUhPTxe7tEZ39OhRhISE4NSpUzh06BDUajWeeeYZFBQUiF1akzt79iz+9a9/oXfv3mKX0qTu37+PwYMHw8zMDD/++CP+/PNPfPjhh2jfvr3YpTWZd999F5s3b8bGjRtx+fJlvPvuu3jvvfewYcMGsUtrFAUFBfDx8cGmTZuqff29997Dxx9/jC1btuD06dOwtLREYGAgioqKmrjSxlPbZ1BYWIhz585h+fLlOHfuHHbv3o2EhAQ8//zzIlTaOB72O1Bmz549OHXq1ENvD2JUAjUb/fv3F0JCQvTLGo1GcHV1FSIiIkSsShzp6ekCAOHo0aNil9Kk8vLyhC5dugiHDh0SnnzySWH+/Plil9RkFi9eLAwZMkTsMkT13HPPCTNnzjRYN3bsWGHKlCkiVdR0AAh79uzRL2u1WsHZ2Vl4//339euys7MFuVwufP311yJU2PgqfwbVOXPmjABASEpKapqimlBN53/79m3Bzc1NuHTpkuDh4SF89NFHTVIPW5CaieLiYsTGxmLkyJH6dVKpFCNHjsTJkydFrEwcOTk5AABbW1uRK2laISEheO655wx+D9qKffv2wd/fHy+99BIcHR3Rp08ffPrpp2KX1aQGDRqEmJgYXLlyBQBw/vx5HDt2DKNGjRK5sqaXmJiI1NRUg/8XlEolAgIC2uTfxDI5OTmQSCRo166d2KU0Ca1Wi6lTp+If//gHevTo0aTHFvVmtVQuMzMTGo3G4Ka6AODk5IT4+HiRqhKHVqvFggULMHjwYPTs2VPscprMjh07cO7cOZw9e1bsUkRx48YNbN68GWFhYXjzzTdx9uxZ/P3vf4dMJkNwcLDY5TWJJUuWIDc3F97e3jAxMYFGo8GaNWswZcoUsUtrcqmpqQBQ7d/EstfamqKiIixevBiTJk1qM/dne/fdd2Fqaoq///3vTX5sBiRqdkJCQnDp0iUcO3ZM7FKazK1btzB//nwcOnQICoVC7HJEodVq4e/vj7Vr1wIA+vTpg0uXLmHLli1tJiB98803+OqrrxAdHY0ePXogLi4OCxYsgKura5v5DKh6arUaL7/8MgRBwObNm8Uup0nExsZi/fr1OHfuHCQSSZMfn5fYmgl7e3uYmJggLS3NYH1aWhqcnZ1FqqrphYaG4vvvv8eRI0fw2GOPiV1Ok4mNjUV6ejr69u0LU1NTmJqa4ujRo/j4449hamoKjUYjdomNzsXFBY8//rjBuu7duyM5OVmkipreP/7xDyxZsgQTJ05Er169MHXqVCxcuBARERFil9bkyv7utfW/iUB5OEpKSsKhQ4faTOvRr7/+ivT0dHTo0EH/dzEpKQmvv/46PD09G/34DEjNhEwmg5+fH2JiYvTrtFotYmJiMHDgQBEraxqCICA0NBR79uzBTz/9hI4dO4pdUpMaMWIELl68iLi4OP3D398fU6ZMQVxcHExMTMQusdENHjy4ytQOV65cgYeHh0gVNb3CwkJIpYZ/lk1MTKDVakWqSDwdO3aEs7Ozwd/E3NxcnD59uk38TSxTFo6uXr2Kw4cPw87OTuySmszUqVNx4cIFg7+Lrq6u+Mc//oGDBw82+vF5ia0ZCQsLQ3BwMPz9/dG/f39ERkaioKAAM2bMELu0RhcSEoLo6Gh89913sLa21vcxUCqVMDc3F7m6xmdtbV2lv5WlpSXs7OzaTD+shQsXYtCgQVi7di1efvllnDlzBv/+97/x73//W+zSmkxQUBDWrFmDDh06oEePHvj999+xbt06zJw5U+zSGkV+fj6uXbumX05MTERcXBxsbW3RoUMHLFiwAP/85z/RpUsXdOzYEcuXL4erqyvGjBkjXtFGVttn4OLigvHjx+PcuXP4/vvvodFo9H8bbW1tIZPJxCrbaB72O1A5EJqZmcHZ2RndunVr/OKaZKwc1dmGDRuEDh06CDKZTOjfv79w6tQpsUtqEgCqfURFRYldmmja2jB/QRCE//73v0LPnj0FuVwueHt7C//+97/FLqlJ5ebmCvPnzxc6dOggKBQKoVOnTsKyZcsElUoldmmN4siRI9X+fx8cHCwIgm6o//LlywUnJydBLpcLI0aMEBISEsQt2shq+wwSExNr/Nt45MgRsUs3iof9DlTWlMP8JYLQSqdoJSIiImog9kEiIiIiqoQBiYiIiKgSBiQiIiKiShiQiIiIiCphQCIiIiKqhAGJiIiIqBIGJCIiIqJKGJCIiIiIKmFAIiIyEolEgr1794pdBhEZAQMSEbUK06dPh0QiqfJ49tlnxS6NiFog3qyWiFqNZ599FlFRUQbr5HK5SNUQUUvGFiQiajXkcjmcnZ0NHu3btwegu/y1efNmjBo1Cubm5ujUqRP+85//GLz/4sWLGD58OMzNzWFnZ4e5c+ciPz/fYJtt27ahR48ekMvlcHFxQWhoqMHrmZmZePHFF2FhYYEuXbpg3759jXvSRNQoGJCIqM1Yvnw5xo0bh/Pnz2PKlCmYOHEiLl++DAAoKChAYGAg2rdvj7Nnz2LXrl04fPiwQQDavHkzQkJCMHfuXFy8eBH79u1D586dDY7x1ltv4eWXX8aFCxcwevRoTJkyBffu3WvS8yQiIxCIiFqB4OBgwcTERLC0tDR4rFmzRhAEQQAgvPbaawbvCQgIEP76178KgiAI//73v4X27dsL+fn5+tf3798vSKVSITU1VRAEQXB1dRWWLVtWYw0AhPDwcP1yfn6+AED48ccfjXaeRNQ02AeJiFqNp556Cps3bzZYZ2trq38+cOBAg9cGDhyIuLg4AMDly5fh4+MDS0tL/euDBw+GVqtFQkICJBIJ7ty5gxEjRtRaQ+/evfXPLS0tYWNjg/T09IaeEhGJhAGJiFoNS0vLKpe8jMXc3LxO25mZmRksSyQSaLXaxiiJiBoR+yARUZtx6tSpKsvdu3cHAHTv3h3nz59HQUGB/vXjx49DKpWiW7dusLa2hqenJ2JiYpq0ZiISB1uQiKjVUKlUSE1NNVhnamoKe3t7AMCuXbvg7++PIUOG4KuvvsKZM2ewdetWAMCUKVOwcuVKBAcHY9WqVcjIyMC8efMwdepUODk5AQBWrVqF1157DY6Ojhg1ahTy8vJw/PhxzJs3r2lPlIgaHQMSEbUaBw4cgIuLi8G6bt26IT4+HoBuhNmOHTvwt7/9DS4uLvj666/x+OOPAwAsLCxw8OBBzJ8/H/369YOFhQXGjRuHdevW6fcVHByMoqIifPTRR1i0aBHs7e0xfvz4pjtBImoyEkEQBLGLICJqbBKJBHv27MGYMWPELoWIWgD2QSIiIiKqhAGJiIiIqBL2QSKiNoG9CYioPtiCRERERFQJAxIRERFRJQxIRERERJUwIBERERFVwoBEREREVAkDEhEREVElDEhERERElTAgEREREVXy/wIABpTvmjtfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a99b6c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n",
      "Confusion Matrix:\n",
      "[[ 971    0    1    1    0    1    2    1    2    1]\n",
      " [   0 1126    3    1    0    1    2    0    2    0]\n",
      " [   2    3 1007    5    2    0    1    6    5    1]\n",
      " [   1    0    3  989    0    5    0    4    2    6]\n",
      " [   0    1    2    0  961    0    8    1    0    9]\n",
      " [   2    0    0    1    1  877    4    1    2    4]\n",
      " [   4    2    0    1    4    3  942    0    2    0]\n",
      " [   1    6    7    3    0    0    0 1005    0    6]\n",
      " [   4    1    1    3    9    7    2    3  939    5]\n",
      " [   2    2    0    4    6    8    0    4    2  981]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99       980\n",
      "           1       0.99      0.99      0.99      1135\n",
      "           2       0.98      0.98      0.98      1032\n",
      "           3       0.98      0.98      0.98      1010\n",
      "           4       0.98      0.98      0.98       982\n",
      "           5       0.97      0.98      0.98       892\n",
      "           6       0.98      0.98      0.98       958\n",
      "           7       0.98      0.98      0.98      1028\n",
      "           8       0.98      0.96      0.97       974\n",
      "           9       0.97      0.97      0.97      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 预测测试集\n",
    "y_pred = model.predict(x_test)\n",
    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "y_true = np.argmax(y_test, axis=1)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "conf_matrix = confusion_matrix(y_true, y_pred_classes)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)\n",
    "\n",
    "# 绘制混淆矩阵的热图\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=range(10), yticklabels=range(10))\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# 打印分类报告\n",
    "class_report = classification_report(y_true, y_pred_classes)\n",
    "print(\"Classification Report:\")\n",
    "print(class_report)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf0f1a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建Tkinter窗口\n",
    "from tkinter import Tk, Button, Label, filedialog\n",
    "from PIL import Image, ImageTk\n",
    "\n",
    "root = Tk()\n",
    "root.title(\"手写数字识别\")\n",
    "root.geometry(\"400x400\")\n",
    "\n",
    "def load_image():\n",
    "    file_path = filedialog.askopenfilename()\n",
    "    if file_path:\n",
    "        try:\n",
    "            # 打开图片并转换为灰度\n",
    "            img = Image.open(file_path).convert('L')\n",
    "            # 调整大小为 28x28\n",
    "            img = img.resize((28, 28), Image.LANCZOS)\n",
    "            # 转换为 numpy 数组\n",
    "            img_array = np.array(img)\n",
    "            # 反色处理（如果背景是黑色）  虽然不知道为什么，但是添加了反色处理之后，准确率提升了\n",
    "            img_array = 255 - img_array\n",
    "            # 归一化到 [0, 1]\n",
    "            img_array = img_array / 255.0\n",
    "            # 调整形状为 (1, 28, 28, 1)\n",
    "            img_array = img_array.reshape(1, 28, 28, 1)\n",
    "            \n",
    "            # 显示选择的图片\n",
    "            img_display = Image.fromarray((img_array[0, :, :, 0] * 255).astype(np.uint8))\n",
    "            img_display = img_display.resize((140, 140), Image.LANCZOS)\n",
    "            tk_img = ImageTk.PhotoImage(img_display)\n",
    "            panel.config(image=tk_img)\n",
    "            panel.image = tk_img\n",
    "            \n",
    "            # 预测数字\n",
    "            pred = model.predict(img_array)\n",
    "            pred_class = np.argmax(pred)\n",
    "            pred_prob = np.max(pred)\n",
    "            result_label.config(text=f\"预测结果: {pred_class}\\n置信度: {pred_prob:.2f}\")\n",
    "        except Exception as e:\n",
    "            result_label.config(text=f\"错误: 无法处理图片\\n{e}\")\n",
    "\n",
    "# 添加按钮和标签\n",
    "btn = Button(root, text=\"选择图片\", command=load_image)\n",
    "btn.pack(pady=20)\n",
    "\n",
    "panel = Label(root)\n",
    "panel.pack(pady=20)\n",
    "\n",
    "result_label = Label(root, text=\"预测结果: \", font=(\"Helvetica\", 16))\n",
    "result_label.pack(pady=20)\n",
    "\n",
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0cce39c-b575-4edd-9ab6-d40d9d71292a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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